scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2013 ◽  
Vol 10 (8) ◽  
pp. 13753-13802
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere-ecosystem carbon dioxide fluxes are well-constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency ecosystem model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely-sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all of the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Evaluating terrestrial CO<sub>2</sub> flux diagnoses and uncertainties from a simple land surface model and its residuals

2014 ◽  
Vol 11 (2) ◽  
pp. 217-235 ◽  
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller
Keyword(s):  
Carbon Dioxide ◽  
Parameter Estimation ◽  
North American ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Ecosystem Respiration ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Considerable Uncertainty

Abstract. Global terrestrial atmosphere–ecosystem carbon dioxide fluxes are well constrained by the concentration and isotopic composition of atmospheric carbon dioxide. In contrast, considerable uncertainty persists surrounding regional contributions to the net global flux as well as the impacts of atmospheric and biological processes that drive the net flux. These uncertainties severely limit our ability to make confident predictions of future terrestrial biological carbon fluxes. Here we use a simple light-use efficiency land surface model (the Vegetation Photosynthesis Respiration Model, VPRM) driven by remotely sensed temperature, moisture, and phenology to diagnose North American gross ecosystem exchange (GEE), ecosystem respiration, and net ecosystem exchange (NEE) for the period 2001 to 2006. We optimize VPRM parameters to eddy covariance (EC) NEE observations from 65 North American FluxNet sites. We use a separate set of 27 cross-validation FluxNet sites to evaluate a range of spatial and temporal resolutions for parameter estimation. With these results we demonstrate that different spatial and temporal groupings of EC sites for parameter estimation achieve similar sum of squared residuals values through radically different spatial patterns of NEE. We also derive a regression model to estimate observed VPRM errors as a function of VPRM NEE, temperature, and precipitation. Because this estimate is based on model-observation residuals it is comprehensive of all the error sources present in modeled fluxes. We find that 1 km interannual variability in VPRM NEE is of similar magnitude to estimated 1 km VPRM NEE errors.

scholarly journals Interannual variability in Australia's terrestrial carbon cycle constrained by multiple observation types

2016 ◽  
Vol 13 (23) ◽  
pp. 6363-6383 ◽  
Author(s):  
Cathy M. Trudinger ◽  
Vanessa Haverd ◽  
Peter R. Briggs ◽  
Josep G. Canadell
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Net Primary Production ◽  
Land Surface Model ◽  
Heterotrophic Respiration ◽  
Arid Ecosystems ◽  
Surface Model ◽  
Calibration Data ◽  
Terrestrial Carbon

Abstract. Recent studies have shown that semi-arid ecosystems in Australia may be responsible for a significant part of the interannual variability in the global concentration of atmospheric carbon dioxide. Here we use a multiple constraints approach to calibrate a land surface model of Australian terrestrial carbon and water cycles, with a focus on interannual variability. We use observations of carbon and water fluxes at 14 OzFlux sites, as well as data on carbon pools, litterfall and streamflow. We include calibration of the function describing the response of heterotrophic respiration to soil moisture. We also explore the effect on modelled interannual variability of parameter equifinality, whereby multiple combinations of parameters can give an equally acceptable fit to the calibration data. We estimate interannual variability of Australian net ecosystem production (NEP) of 0.12–0.21 PgC yr−1 (1σ) over 1982–2013, with a high anomaly of 0.43–0.67 PgC yr−1 in 2011 relative to this period associated with exceptionally wet conditions following a prolonged drought. The ranges are due to the effect on calculated NEP anomaly of parameter equifinality, with similar contributions from equifinality in parameters associated with net primary production (NPP) and heterotrophic respiration. Our range of results due to parameter equifinality demonstrates how errors can be underestimated when a single parameter set is used.

scholarly journals WRFv3.2-SPAv2: development and validation of a coupled ecosystem–atmosphere model, scaling from surface fluxes of CO<sub>2</sub> and energy to atmospheric profiles

2013 ◽  
Vol 6 (4) ◽  
pp. 1079-1093 ◽  
Author(s):  
T. L. Smallman ◽  
J. B. Moncrieff ◽  
M. Williams
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Terrestrial Ecosystem ◽  
Surface Fluxes ◽  
Surface Model ◽  
Terrestrial Ecosystem Model ◽  
Atmospheric Profiles

Abstract. The Weather Research and Forecasting meteorological (WRF) model has been coupled to the Soil–Plant–Atmosphere (SPA) terrestrial ecosystem model, to produce WRF-SPA. SPA generates realistic land–atmosphere exchanges through fully coupled hydrological, carbon and energy cycles. The addition of a~land surface model (SPA) capable of modelling biospheric CO2 exchange allows WRF-SPA to be used for investigating the feedbacks between biosphere carbon balance, meteorology, and land use and land cover change. We have extensively validated WRF-SPA using multi-annual observations of air temperature, turbulent fluxes, net radiation and net ecosystem exchange of CO2 at three sites, representing the dominant vegetation types in Scotland (forest, managed grassland and arable agriculture). For example air temperature is well simulated across all sites (forest R2 = 0.92, RMSE = 1.7 °C, bias = 0.88 °C; managed grassland R2 = 0.73, RMSE = 2.7 °C, bias = −0.30 °C; arable agriculture R2 = 0.82, RMSE = 2.2 °C, bias = 0.46 °C; RMSE, root mean square error). WRF-SPA generates more realistic seasonal behaviour at the site level compared to an unmodified version of WRF, such as improved simulation of seasonal transitions in latent heat flux in arable systems. WRF-SPA also generates realistic seasonal CO2 exchanges across all sites. WRF-SPA is also able to realistically model atmospheric profiles of CO2 over Scotland, spanning a 3 yr period (2004–2006), capturing both profile structure, indicating realistic transport, and magnitude (model–data residual

scholarly journals Improving terrestrial CO<sub>2</sub> flux diagnosis using spatial structure in land surface model residuals

2012 ◽  
Vol 9 (6) ◽  
pp. 7073-7116
Author(s):  
T. W. Hilton ◽  
K. J. Davis ◽  
K. Keller ◽  
N. M. Urban
Keyword(s):  
Spatial Structure ◽  
Covariance Matrix ◽  
North American ◽  
Data Model ◽  
Land Surface ◽  
Land Surface Model ◽  
Co2 Flux ◽  
Surface Model ◽  
Residual Covariance ◽  
Model Residuals

Abstract. We evaluate spatial structure in North American CO2 flux observations using a simple diagnostic land surface model. The Vegetation Photosynthesis Respiration Model (VPRM) calculates net ecosystem exchange (NEE) using locally observed temperature and photosynthetically active radiation (PAR) along with satellite-derived phenology and moisture. We use observed NEE from a group of 65 North American eddy covariance tower sites spanning North America to estimate VPRM parameters for these sites. We investigate spatial coherence in regional CO2 fluxes at several different time scales by using geostatistical methods to examine the spatial structure of model data-model residuals. We find that persistent spatial structure does exist in the data-model residuals at a length scale of approximately 1000 km. This spatial structure defines a flux-tower-based VPRM residual covariance matrix. The residual covariance matrix is useful in constructing prior fluxes for atmospheric CO2 concentration inversion calculations, as well as for constructing a VPRM North American CO2 flux map optimized to eddy covariance observations. Finally, the estimated VPRM parameter values do not separate clearly by plant functional type (PFT). This calls into question whether PFTs partition ecosystems by carbon cycle participation when the viewing lens is a simple model.

scholarly journals Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

2010 ◽  
Vol 7 (8) ◽  
pp. 2397-2417 ◽  
Author(s):  
H. W. Ter Maat ◽  
R. W. A. Hutjes ◽  
F. Miglietta ◽  
B. Gioli ◽  
F. C. Bosveld ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Land Surface Model ◽  
Atmospheric Model ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Large Variability ◽  
Regional Atmospheric Modelling System

Abstract. This paper is a case study to investigate what the main controlling factors are that determine atmospheric carbon dioxide content for a region in the centre of The Netherlands. We use the Regional Atmospheric Modelling System (RAMS), coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C), and including also submodels for urban and marine fluxes, which in principle should include the dominant mechanisms and should be able to capture the relevant dynamics of the system. To validate the model, observations are used that were taken during an intensive observational campaign in central Netherlands in summer 2002. These include flux-tower observations and aircraft observations of vertical profiles and spatial fluxes of various variables. The simulations performed with the coupled regional model (RAMS-SWAPS-C) are in good qualitative agreement with the observations. The station validation of the model demonstrates that the incoming shortwave radiation and surface fluxes of water and CO2 are well simulated. The comparison against aircraft data shows that the regional meteorology (i.e. wind, temperature) is captured well by the model. Comparing spatially explicitly simulated fluxes with aircraft observed fluxes we conclude that in general latent heat fluxes are underestimated by the model compared to the observations but that the latter exhibit large variability within all flights. Sensitivity experiments demonstrate the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same tests also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

Regional Parameter Estimation of the VIC Land Surface Model: Methodology and Application to River Basins in China

2007 ◽  
Vol 8 (3) ◽  
pp. 447-468 ◽  
Author(s):  
Zhenghui Xie ◽  
Fei Yuan ◽  
Qingyun Duan ◽  
Jing Zheng ◽  
Miaoling Liang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
River Basin ◽  
Land Surface ◽  
Land Surface Model ◽  
River Basins ◽  
Large River ◽  
Surface Model ◽  
Parameter Estimates ◽  
Climatic Zones ◽  
Streamflow Simulation

Abstract This paper presents a methodology for regional parameter estimation of the three-layer Variable Infiltration Capacity (VIC-3L) land surface model with the goal of improving the streamflow simulation for river basins in China. This methodology is designed to obtain model parameter estimates from a limited number of calibrated basins and then regionalize them to uncalibrated basins based on climate characteristics and large river basin domains, and ultimately to continental China. Fourteen basins from different climatic zones and large river basins were chosen for model calibration. For each of these basins, seven runoff-related model parameters were calibrated using a systematic manual calibration approach. These calibrated parameters were then transferred within the climate and large river basin zones or climatic zones to the uncalibrated basins. To test the efficiency of the parameter regionalization method, a verification study was conducted on 19 independent river basins in China. Overall, the regionalized parameters, when evaluated against the a priori parameter estimates, were able to reduce the model bias by 0.4%–249.8% and relative root-mean-squared error by 0.2%–119.1% and increase the Nash–Sutcliffe efficiency of the streamflow simulation by 1.9%–31.7% for most of the tested basins. The transferred parameters were then used to perform a hydrological simulation over all of China so as to test the applicability of the regionalized parameters on a continental scale. The continental simulation results agree well with the observations at regional scales, indicating that the tested regionalization method is a promising scheme for parameter estimation for ungauged basins in China.

scholarly journals Evaluation of the Complementary Relationship Using Noah Land Surface Model and North American Regional Reanalysis (NARR) Data to Estimate Evapotranspiration in Semiarid Ecosystems

2013 ◽  
Vol 14 (1) ◽  
pp. 345-359 ◽  
Author(s):  
W. Thilini Jaksa ◽  
Venkataramana Sridhar ◽  
Justin L. Huntington ◽  
Mandar Khanal
Keyword(s):  
North American ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
B Values ◽  
Complementary Relationship ◽  
North American Regional Reanalysis ◽  
Noah Land Surface Model ◽  
Regional Reanalysis ◽  
Mean Square Errors

Abstract Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ETp) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection–aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ETp, and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day−1.

scholarly journals Coupling the high complexity land surface model ACASA to the mesoscale model WRF

2014 ◽  
Vol 7 (3) ◽  
pp. 2829-2875 ◽  
Author(s):  
L. Xu ◽  
R. D. Pyles ◽  
K. T. Paw U ◽  
S. H. Chen ◽  
E. Monier
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
Mesoscale Model ◽  
Land Surface Model ◽  
Carbon Dioxide Concentration ◽  
Atmospheric Model ◽  
Dew Point ◽  
Surface Model ◽  
High Complexity ◽  
Surface Conditions

Abstract. In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional atmospheric model with high spatial and temporal resolutions, the land surface schemes available in WRF are simple and lack the capability to simulate carbon dioxide, for example, the popular NOAH LSM. ACASA is a complex multilayer land surface model with interactive canopy physiology and full surface hydrological processes. It allows microenvironmental variables such as air and surface temperatures, wind speed, humidity, and carbon dioxide concentration to vary vertically. Simulations of surface conditions such as air temperature, dew point temperature, and relative humidity from WRF–ACASA and WRF–NOAH are compared with surface observation from over 700 meteorological stations in California. Results show that the increase in complexity in the WRF–ACASA model not only maintains model accuracy, it also properly accounts for the dominant biological and physical processes describing ecosystem-atmosphere interactions that are scientifically valuable. The different complexities of physical and physiological processes in the WRF–ACASA and WRF–NOAH models also highlight the impacts of different land surface and model components on atmospheric and surface conditions.

scholarly journals Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

2022 ◽  
Vol 3 ◽  
Author(s):  
Azbina Rahman ◽  
Xinxuan Zhang ◽  
Paul Houser ◽  
Timothy Sauer ◽  
Viviana Maggioni
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Land Surface Model ◽  
Net Ecosystem Exchange ◽  
Surface Model ◽  
Random Errors ◽  
Area Index ◽  
Leaf Mass ◽  
The Impact

As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

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